Overview

Brought to you by YData

Dataset statistics

Number of variables81
Number of observations1460
Missing cells7829
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory2.7 KiB

Variable types

Numeric30
Categorical50
Boolean1

Alerts

MSZoning is highly imbalanced (56.9%)Imbalance
Street is highly imbalanced (96.2%)Imbalance
LandContour is highly imbalanced (68.3%)Imbalance
Utilities is highly imbalanced (99.2%)Imbalance
LandSlope is highly imbalanced (78.8%)Imbalance
Condition1 is highly imbalanced (71.7%)Imbalance
Condition2 is highly imbalanced (96.4%)Imbalance
BldgType is highly imbalanced (59.4%)Imbalance
RoofStyle is highly imbalanced (65.1%)Imbalance
RoofMatl is highly imbalanced (94.4%)Imbalance
ExterCond is highly imbalanced (72.8%)Imbalance
BsmtCond is highly imbalanced (75.8%)Imbalance
BsmtFinType2 is highly imbalanced (70.1%)Imbalance
Heating is highly imbalanced (92.7%)Imbalance
CentralAir is highly imbalanced (65.3%)Imbalance
Electrical is highly imbalanced (78.2%)Imbalance
BsmtHalfBath is highly imbalanced (79.7%)Imbalance
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
Functional is highly imbalanced (81.9%)Imbalance
GarageQual is highly imbalanced (85.2%)Imbalance
GarageCond is highly imbalanced (87.6%)Imbalance
PavedDrive is highly imbalanced (69.9%)Imbalance
MiscFeature is highly imbalanced (70.7%)Imbalance
SaleType is highly imbalanced (75.3%)Imbalance
SaleCondition is highly imbalanced (62.5%)Imbalance
LotFrontage has 259 (17.7%) missing valuesMissing
Alley has 1369 (93.8%) missing valuesMissing
MasVnrType has 872 (59.7%) missing valuesMissing
BsmtQual has 37 (2.5%) missing valuesMissing
BsmtCond has 37 (2.5%) missing valuesMissing
BsmtExposure has 38 (2.6%) missing valuesMissing
BsmtFinType1 has 37 (2.5%) missing valuesMissing
BsmtFinType2 has 38 (2.6%) missing valuesMissing
FireplaceQu has 690 (47.3%) missing valuesMissing
GarageType has 81 (5.5%) missing valuesMissing
GarageYrBlt has 81 (5.5%) missing valuesMissing
GarageFinish has 81 (5.5%) missing valuesMissing
GarageQual has 81 (5.5%) missing valuesMissing
GarageCond has 81 (5.5%) missing valuesMissing
PoolQC has 1453 (99.5%) missing valuesMissing
Fence has 1179 (80.8%) missing valuesMissing
MiscFeature has 1406 (96.3%) missing valuesMissing
MiscVal is highly skewed (γ1 = 24.47679419)Skewed
Id is uniformly distributedUniform
Id has unique valuesUnique
MasVnrArea has 861 (59.0%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros
MiscVal has 1408 (96.4%) zerosZeros

Reproduction

Analysis started2025-11-07 18:48:29.728761
Analysis finished2025-11-07 18:51:57.826991
Duration3 minutes and 28.1 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Uniform  Unique 

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:51:58.052734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.61001
Coefficient of variation (CV)0.57715265
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2025-11-07T15:51:58.387593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:51:58.653011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2025-11-07T15:51:58.892431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20536
36.7%
60299
20.5%
50144
 
9.9%
12087
 
6.0%
3069
 
4.7%
16063
 
4.3%
7060
 
4.1%
8058
 
4.0%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.2%
ValueCountFrequency (%)
20536
36.7%
3069
 
4.7%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
60299
20.5%
7060
 
4.1%
7516
 
1.1%
8058
 
4.0%
8520
 
1.4%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
 
4.3%
12087
 
6.0%
9052
 
3.6%
8520
 
1.4%
8058
 
4.0%
7516
 
1.1%
7060
 
4.1%
60299
20.5%

MSZoning
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.3 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0342466
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL1151
78.8%
RM218
 
14.9%
FV65
 
4.5%
RH16
 
1.1%
C (all)10
 
0.7%

Length

2025-11-07T15:51:59.183549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:51:59.448261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
rl1151
78.3%
rm218
 
14.8%
fv65
 
4.4%
rh16
 
1.1%
c10
 
0.7%
all10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

LotFrontage
Real number (ℝ)

Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:51:59.722675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2025-11-07T15:52:00.060189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60143
 
9.8%
7070
 
4.8%
8069
 
4.7%
5057
 
3.9%
7553
 
3.6%
6544
 
3.0%
8540
 
2.7%
7825
 
1.7%
9023
 
1.6%
2123
 
1.6%
Other values (100)654
44.8%
(Missing)259
 
17.7%
ValueCountFrequency (%)
2123
1.6%
2419
1.3%
306
 
0.4%
325
 
0.3%
331
 
0.1%
3410
0.7%
359
 
0.6%
366
 
0.4%
375
 
0.3%
381
 
0.1%
ValueCountFrequency (%)
3132
0.1%
1821
0.1%
1742
0.1%
1681
0.1%
1601
0.1%
1531
0.1%
1521
0.1%
1501
0.1%
1491
0.1%
1441
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:00.408798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2025-11-07T15:52:00.710144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
900014
 
1.0%
840014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
91008
 
0.5%
81258
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

Street
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
Pave
1454 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave1454
99.6%
Grvl6
 
0.4%

Length

2025-11-07T15:52:00.986764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:01.206792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pave1454
99.6%
grvl6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Alley
Categorical

Missing 

Distinct2
Distinct (%)2.2%
Missing1369
Missing (%)93.8%
Memory size91.1 KiB
Grvl
50 
Pave
41 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters364
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave

Common Values

ValueCountFrequency (%)
Grvl50
 
3.4%
Pave41
 
2.8%
(Missing)1369
93.8%

Length

2025-11-07T15:52:01.415210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:01.638297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
grvl50
54.9%
pave41
45.1%

Most occurring characters

ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size85.7 KiB
Reg
925 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg925
63.4%
IR1484
33.2%
IR241
 
2.8%
IR310
 
0.7%

Length

2025-11-07T15:52:01.883036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:02.112955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
reg925
63.4%
ir1484
33.2%
ir241
 
2.8%
ir310
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

LandContour
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size85.7 KiB
Lvl
1311 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl1311
89.8%
Bnk63
 
4.3%
HLS50
 
3.4%
Low36
 
2.5%

Length

2025-11-07T15:52:02.385509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:02.612324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
lvl1311
89.8%
bnk63
 
4.3%
hls50
 
3.4%
low36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Utilities
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
AllPub
1459 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8760
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub1459
99.9%
NoSeWa1
 
0.1%

Length

2025-11-07T15:52:02.843093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:03.054298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
allpub1459
99.9%
nosewa1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size89.9 KiB
Inside
1052 
Corner
263 
CulDSac
 
94
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.959589
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside1052
72.1%
Corner263
 
18.0%
CulDSac94
 
6.4%
FR247
 
3.2%
FR34
 
0.3%

Length

2025-11-07T15:52:03.313323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:03.572027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
inside1052
72.1%
corner263
 
18.0%
culdsac94
 
6.4%
fr247
 
3.2%
fr34
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

LandSlope
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size85.7 KiB
Gtl
1382 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl1382
94.7%
Mod65
 
4.5%
Sev13
 
0.9%

Length

2025-11-07T15:52:03.857551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:04.093183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
gtl1382
94.7%
mod65
 
4.5%
sev13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Neighborhood
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size90.7 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.4945205
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes225
15.4%
CollgCr150
 
10.3%
OldTown113
 
7.7%
Edwards100
 
6.8%
Somerst86
 
5.9%
Gilbert79
 
5.4%
NridgHt77
 
5.3%
Sawyer74
 
5.1%
NWAmes73
 
5.0%
SawyerW59
 
4.0%
Other values (15)424
29.0%

Length

2025-11-07T15:52:04.342217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names225
15.4%
collgcr150
 
10.3%
oldtown113
 
7.7%
edwards100
 
6.8%
somerst86
 
5.9%
gilbert79
 
5.4%
nridght77
 
5.3%
sawyer74
 
5.1%
nwames73
 
5.0%
sawyerw59
 
4.0%
Other values (15)424
29.0%

Most occurring characters

ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Condition1
Categorical

Imbalance 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size87.3 KiB
Norm
1260 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.1212329
Min length4

Characters and Unicode

Total characters6017
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm1260
86.3%
Feedr81
 
5.5%
Artery48
 
3.3%
RRAn26
 
1.8%
PosN19
 
1.3%
RRAe11
 
0.8%
PosA8
 
0.5%
RRNn5
 
0.3%
RRNe2
 
0.1%

Length

2025-11-07T15:52:04.663236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:04.947297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
norm1260
86.3%
feedr81
 
5.5%
artery48
 
3.3%
rran26
 
1.8%
posn19
 
1.3%
rrae11
 
0.8%
posa8
 
0.5%
rrnn5
 
0.3%
rrne2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Condition2
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
Norm
1445 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.0068493
Min length4

Characters and Unicode

Total characters5850
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm1445
99.0%
Feedr6
 
0.4%
Artery2
 
0.1%
RRNn2
 
0.1%
PosN2
 
0.1%
PosA1
 
0.1%
RRAn1
 
0.1%
RRAe1
 
0.1%

Length

2025-11-07T15:52:05.248595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:05.535623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
norm1445
99.0%
feedr6
 
0.4%
artery2
 
0.1%
rrnn2
 
0.1%
posn2
 
0.1%
posa1
 
0.1%
rran1
 
0.1%
rrae1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

BldgType
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.5 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2993151
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam1220
83.6%
TwnhsE114
 
7.8%
Duplex52
 
3.6%
Twnhs43
 
2.9%
2fmCon31
 
2.1%

Length

2025-11-07T15:52:05.842327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:06.100211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1fam1220
83.6%
twnhse114
 
7.8%
duplex52
 
3.6%
twnhs43
 
2.9%
2fmcon31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

HouseStyle
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size89.8 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9109589
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story726
49.7%
2Story445
30.5%
1.5Fin154
 
10.5%
SLvl65
 
4.5%
SFoyer37
 
2.5%
1.5Unf14
 
1.0%
2.5Unf11
 
0.8%
2.5Fin8
 
0.5%

Length

2025-11-07T15:52:06.377621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:06.684370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1story726
49.7%
2story445
30.5%
1.5fin154
 
10.5%
slvl65
 
4.5%
sfoyer37
 
2.5%
1.5unf14
 
1.0%
2.5unf11
 
0.8%
2.5fin8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:06.939335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2025-11-07T15:52:07.157156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
943
 
2.9%
1018
 
1.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%
5397
27.2%
4116
 
7.9%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:07.362304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2025-11-07T15:52:07.618044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%
457
 
3.9%
325
 
1.7%
25
 
0.3%
11
 
0.1%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:07.896852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2025-11-07T15:52:08.222247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%
200564
4.4%
200454
3.7%
200345
3.1%
200223
 
1.6%
200120
 
1.4%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:08.528457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2025-11-07T15:52:08.874654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
200573
5.0%
200462
4.2%
200351
3.5%
200248
3.3%
200121
 
1.4%

RoofStyle
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6226027
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable1141
78.2%
Hip286
 
19.6%
Flat13
 
0.9%
Gambrel11
 
0.8%
Mansard7
 
0.5%
Shed2
 
0.1%

Length

2025-11-07T15:52:09.197010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:09.464089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
gable1141
78.2%
hip286
 
19.6%
flat13
 
0.9%
gambrel11
 
0.8%
mansard7
 
0.5%
shed2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

RoofMatl
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size91.4 KiB
CompShg
1434 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.9965753
Min length4

Characters and Unicode

Total characters10215
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg1434
98.2%
Tar&Grv11
 
0.8%
WdShngl6
 
0.4%
WdShake5
 
0.3%
Metal1
 
0.1%
Membran1
 
0.1%
Roll1
 
0.1%
ClyTile1
 
0.1%

Length

2025-11-07T15:52:09.762498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:10.015595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
compshg1434
98.2%
tar&grv11
 
0.8%
wdshngl6
 
0.4%
wdshake5
 
0.3%
metal1
 
0.1%
membran1
 
0.1%
roll1
 
0.1%
clytile1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Exterior1st
Categorical

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size91.3 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.9794521
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd515
35.3%
HdBoard222
15.2%
MetalSd220
15.1%
Wd Sdng206
 
14.1%
Plywood108
 
7.4%
CemntBd61
 
4.2%
BrkFace50
 
3.4%
WdShing26
 
1.8%
Stucco25
 
1.7%
AsbShng20
 
1.4%
Other values (5)7
 
0.5%

Length

2025-11-07T15:52:10.312494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd515
30.9%
hdboard222
13.3%
metalsd220
13.2%
wd206
 
12.4%
sdng206
 
12.4%
plywood108
 
6.5%
cemntbd61
 
3.7%
brkface50
 
3.0%
wdshing26
 
1.6%
stucco25
 
1.5%
Other values (6)27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Exterior2nd
Categorical

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size91.3 KiB
VinylSd
504 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.9732877
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd504
34.5%
MetalSd214
14.7%
HdBoard207
14.2%
Wd Sdng197
 
13.5%
Plywood142
 
9.7%
CmentBd60
 
4.1%
Wd Shng38
 
2.6%
Stucco26
 
1.8%
BrkFace25
 
1.7%
AsbShng20
 
1.4%
Other values (6)27
 
1.8%

Length

2025-11-07T15:52:10.603001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd504
29.6%
wd235
13.8%
metalsd214
12.6%
hdboard207
12.2%
sdng197
 
11.6%
plywood142
 
8.3%
cmentbd60
 
3.5%
shng38
 
2.2%
stucco26
 
1.5%
brkface25
 
1.5%
Other values (8)54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

MasVnrType
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing872
Missing (%)59.7%
Memory size91.1 KiB
BrkFace
445 
Stone
128 
BrkCmn
 
15

Length

Max length7
Median length7
Mean length6.5391156
Min length5

Characters and Unicode

Total characters3845
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowStone
5th rowStone

Common Values

ValueCountFrequency (%)
BrkFace445
30.5%
Stone128
 
8.8%
BrkCmn15
 
1.0%
(Missing)872
59.7%

Length

2025-11-07T15:52:10.913327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:11.146209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
brkface445
75.7%
stone128
 
21.8%
brkcmn15
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:11.391869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2025-11-07T15:52:11.761155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0861
59.0%
728
 
0.5%
1088
 
0.5%
1808
 
0.5%
1207
 
0.5%
167
 
0.5%
3406
 
0.4%
1066
 
0.4%
806
 
0.4%
2006
 
0.4%
Other values (317)529
36.2%
(Missing)8
 
0.5%
ValueCountFrequency (%)
0861
59.0%
12
 
0.1%
111
 
0.1%
141
 
0.1%
167
 
0.5%
182
 
0.1%
221
 
0.1%
241
 
0.1%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
16001
0.1%
13781
0.1%
11701
0.1%
11291
0.1%
11151
0.1%
10471
0.1%
10311
0.1%
9751
0.1%
9221
0.1%
9211
0.1%

ExterQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA906
62.1%
Gd488
33.4%
Ex52
 
3.6%
Fa14
 
1.0%

Length

2025-11-07T15:52:12.138560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:12.382190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta906
62.1%
gd488
33.4%
ex52
 
3.6%
fa14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

ExterCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1282
87.8%
Gd146
 
10.0%
Fa28
 
1.9%
Ex3
 
0.2%
Po1
 
0.1%

Length

2025-11-07T15:52:12.636878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:12.902230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta1282
87.8%
gd146
 
10.0%
fa28
 
1.9%
ex3
 
0.2%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Foundation
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size89.3 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.5157534
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc647
44.3%
CBlock634
43.4%
BrkTil146
 
10.0%
Slab24
 
1.6%
Stone6
 
0.4%
Wood3
 
0.2%

Length

2025-11-07T15:52:13.182637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:13.439501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pconc647
44.3%
cblock634
43.4%
brktil146
 
10.0%
slab24
 
1.6%
stone6
 
0.4%
wood3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

BsmtQual
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size84.4 KiB
TA
649 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA649
44.5%
Gd618
42.3%
Ex121
 
8.3%
Fa35
 
2.4%
(Missing)37
 
2.5%

Length

2025-11-07T15:52:13.701678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:13.947891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta649
45.6%
gd618
43.4%
ex121
 
8.5%
fa35
 
2.5%

Most occurring characters

ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

BsmtCond
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size84.4 KiB
TA
1311 
Gd
 
65
Fa
 
45
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA1311
89.8%
Gd65
 
4.5%
Fa45
 
3.1%
Po2
 
0.1%
(Missing)37
 
2.5%

Length

2025-11-07T15:52:14.233192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:14.455842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta1311
92.1%
gd65
 
4.6%
fa45
 
3.2%
po2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

BsmtExposure
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size84.4 KiB
No
953 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No953
65.3%
Av221
 
15.1%
Gd134
 
9.2%
Mn114
 
7.8%
(Missing)38
 
2.6%

Length

2025-11-07T15:52:14.705311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:14.971303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
no953
67.0%
av221
 
15.5%
gd134
 
9.4%
mn114
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

BsmtFinType1
Categorical

Missing 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size85.8 KiB
Unf
430 
GLQ
418 
ALQ
220 
BLQ
148 
Rec
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4269
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf430
29.5%
GLQ418
28.6%
ALQ220
15.1%
BLQ148
 
10.1%
Rec133
 
9.1%
LwQ74
 
5.1%
(Missing)37
 
2.5%

Length

2025-11-07T15:52:15.229176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:15.478577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
unf430
30.2%
glq418
29.4%
alq220
15.5%
blq148
 
10.4%
rec133
 
9.3%
lwq74
 
5.2%

Most occurring characters

ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

BsmtFinSF1
Real number (ℝ)

Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:15.792997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2025-11-07T15:52:16.083163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
32.0%
2412
 
0.8%
169
 
0.6%
6865
 
0.3%
6625
 
0.3%
205
 
0.3%
9365
 
0.3%
6165
 
0.3%
5604
 
0.3%
5534
 
0.3%
Other values (627)939
64.3%
ValueCountFrequency (%)
0467
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%
18801
0.1%
18101
0.1%
17671
0.1%
17211
0.1%
16961
0.1%

BsmtFinType2
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size85.8 KiB
Unf
1256 
Rec
 
54
LwQ
 
46
BLQ
 
33
ALQ
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4266
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf1256
86.0%
Rec54
 
3.7%
LwQ46
 
3.2%
BLQ33
 
2.3%
ALQ19
 
1.3%
GLQ14
 
1.0%
(Missing)38
 
2.6%

Length

2025-11-07T15:52:16.386230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:16.627771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
unf1256
88.3%
rec54
 
3.8%
lwq46
 
3.2%
blq33
 
2.3%
alq19
 
1.3%
glq14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

BsmtFinSF2
Real number (ℝ)

Zeros 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549315
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:16.914384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.31927
Coefficient of variation (CV)3.4655563
Kurtosis20.113338
Mean46.549315
Median Absolute Deviation (MAD)0
Skewness4.2552611
Sum67962
Variance26023.908
MonotonicityNot monotonic
2025-11-07T15:52:17.249435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01293
88.6%
1805
 
0.3%
3743
 
0.2%
5512
 
0.1%
1472
 
0.1%
2942
 
0.1%
3912
 
0.1%
5392
 
0.1%
962
 
0.1%
4802
 
0.1%
Other values (134)145
 
9.9%
ValueCountFrequency (%)
01293
88.6%
281
 
0.1%
321
 
0.1%
351
 
0.1%
401
 
0.1%
412
 
0.1%
642
 
0.1%
681
 
0.1%
801
 
0.1%
811
 
0.1%
ValueCountFrequency (%)
14741
0.1%
11271
0.1%
11201
0.1%
10851
0.1%
10801
0.1%
10631
0.1%
10611
0.1%
10571
0.1%
10311
0.1%
10291
0.1%

BsmtUnfSF
Real number (ℝ)

Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:17.562668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2025-11-07T15:52:17.903066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3848
 
0.5%
6007
 
0.5%
3007
 
0.5%
5727
 
0.5%
2706
 
0.4%
6256
 
0.4%
6726
 
0.4%
4406
 
0.4%
Other values (770)1280
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
291
 
0.1%
301
 
0.1%
322
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19691
0.1%
19351
0.1%
19261
0.1%
19071
0.1%

TotalBsmtSF
Real number (ℝ)

Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:18.229751image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2025-11-07T15:52:18.592639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
78011
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3847
 
0.5%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

Heating
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
GasA
1428 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006849
Min length4

Characters and Unicode

Total characters5841
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA1428
97.8%
GasW18
 
1.2%
Grav7
 
0.5%
Wall4
 
0.3%
OthW2
 
0.1%
Floor1
 
0.1%

Length

2025-11-07T15:52:18.955124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:19.274659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
gasa1428
97.8%
gasw18
 
1.2%
grav7
 
0.5%
wall4
 
0.3%
othw2
 
0.1%
floor1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex741
50.8%
TA428
29.3%
Gd241
 
16.5%
Fa49
 
3.4%
Po1
 
0.1%

Length

2025-11-07T15:52:19.681629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:20.020280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ex741
50.8%
ta428
29.3%
gd241
 
16.5%
fa49
 
3.4%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

CentralAir
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True1365
93.5%
False95
 
6.5%
2025-11-07T15:52:20.288154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Electrical
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size88.5 KiB
SBrkr
1334 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.9986292
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr1334
91.4%
FuseA94
 
6.4%
FuseF27
 
1.8%
FuseP3
 
0.2%
Mix1
 
0.1%
(Missing)1
 
0.1%

Length

2025-11-07T15:52:20.604919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:20.901738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr1334
91.4%
fusea94
 
6.4%
fusef27
 
1.9%
fusep3
 
0.2%
mix1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:21.157268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2025-11-07T15:52:21.452607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
4837
 
0.5%
9607
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
25151
0.1%
24441
0.1%
24111
0.1%
24021
0.1%

2ndFlrSF
Real number (ℝ)

Zeros 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:21.747063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2025-11-07T15:52:22.009812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0829
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.5%
6728
 
0.5%
6007
 
0.5%
7207
 
0.5%
8966
 
0.4%
8625
 
0.3%
7805
 
0.3%
Other values (407)566
38.8%
ValueCountFrequency (%)
0829
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
2131
 
0.1%
2201
 
0.1%
2241
 
0.1%
2402
 
0.1%
2522
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%
15891
0.1%
15401
0.1%
15381
0.1%
15231
0.1%
15191
0.1%

LowQualFinSF
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:22.279093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2025-11-07T15:52:22.539804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01434
98.2%
803
 
0.2%
3602
 
0.1%
2051
 
0.1%
4791
 
0.1%
3971
 
0.1%
5141
 
0.1%
1201
 
0.1%
4811
 
0.1%
2321
 
0.1%
Other values (14)14
 
1.0%
ValueCountFrequency (%)
01434
98.2%
531
 
0.1%
803
 
0.2%
1201
 
0.1%
1441
 
0.1%
1561
 
0.1%
2051
 
0.1%
2321
 
0.1%
2341
 
0.1%
3602
 
0.1%
ValueCountFrequency (%)
5721
0.1%
5281
0.1%
5151
0.1%
5141
0.1%
5131
0.1%
4811
0.1%
4791
0.1%
4731
0.1%
4201
0.1%
3971
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:22.895380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2025-11-07T15:52:25.189692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
145610
 
0.7%
84810
 
0.7%
12009
 
0.6%
9129
 
0.6%
8168
 
0.5%
10928
 
0.5%
17287
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Length

2025-11-07T15:52:25.428623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:25.718750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

BsmtHalfBath
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Length

2025-11-07T15:52:26.069432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:26.366057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Length

2025-11-07T15:52:26.685064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:26.929482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Length

2025-11-07T15:52:27.162585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:27.376068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:27.585318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2025-11-07T15:52:27.855731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%
2358
24.5%
150
 
3.4%
06
 
0.4%

KitchenAbvGr
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Length

2025-11-07T15:52:28.112670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:28.302501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

KitchenQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA735
50.3%
Gd586
40.1%
Ex100
 
6.8%
Fa39
 
2.7%

Length

2025-11-07T15:52:28.533047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:28.736040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta735
50.3%
gd586
40.1%
ex100
 
6.8%
fa39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:28.933538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2025-11-07T15:52:29.148846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6402
27.5%
7329
22.5%
5275
18.8%
8187
12.8%
497
 
6.6%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
497
 
6.6%
5275
18.8%
6402
27.5%
7329
22.5%
8187
12.8%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
 
3.2%
975
 
5.1%
8187
12.8%
7329
22.5%
6402
27.5%
5275
18.8%
497
 
6.6%

Functional
Categorical

Imbalance 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size85.8 KiB
Typ
1360 
Min2
 
34
Min1
 
31
Mod
 
15
Maj1
 
14
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.0575342
Min length3

Characters and Unicode

Total characters4464
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ1360
93.2%
Min234
 
2.3%
Min131
 
2.1%
Mod15
 
1.0%
Maj114
 
1.0%
Maj25
 
0.3%
Sev1
 
0.1%

Length

2025-11-07T15:52:29.396031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:29.582745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
typ1360
93.2%
min234
 
2.3%
min131
 
2.1%
mod15
 
1.0%
maj114
 
1.0%
maj25
 
0.3%
sev1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Length

2025-11-07T15:52:29.735853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:29.867224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

FireplaceQu
Categorical

Missing 

Distinct5
Distinct (%)0.6%
Missing690
Missing (%)47.3%
Memory size87.6 KiB
Gd
380 
TA
313 
Fa
 
33
Ex
 
24
Po
 
20

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1540
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
Gd380
26.0%
TA313
21.4%
Fa33
 
2.3%
Ex24
 
1.6%
Po20
 
1.4%
(Missing)690
47.3%

Length

2025-11-07T15:52:30.003631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:30.136503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
gd380
49.4%
ta313
40.6%
fa33
 
4.3%
ex24
 
3.1%
po20
 
2.6%

Most occurring characters

ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

GarageType
Categorical

Missing 

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size90.1 KiB
Attchd
870 
Detchd
387 
BuiltIn
88 
Basment
 
19
CarPort
 
9

Length

Max length7
Median length6
Mean length6.0841189
Min length6

Characters and Unicode

Total characters8390
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd870
59.6%
Detchd387
26.5%
BuiltIn88
 
6.0%
Basment19
 
1.3%
CarPort9
 
0.6%
2Types6
 
0.4%
(Missing)81
 
5.5%

Length

2025-11-07T15:52:30.282985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:30.438619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
attchd870
63.1%
detchd387
28.1%
builtin88
 
6.4%
basment19
 
1.4%
carport9
 
0.7%
2types6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

GarageYrBlt
Real number (ℝ)

Missing 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:30.601848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.689725
Coefficient of variation (CV)0.012478973
Kurtosis-0.418341
Mean1978.5062
Median Absolute Deviation (MAD)21
Skewness-0.64941462
Sum2728360
Variance609.58251
MonotonicityNot monotonic
2025-11-07T15:52:30.784087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200565
 
4.5%
200659
 
4.0%
200453
 
3.6%
200350
 
3.4%
200749
 
3.4%
197735
 
2.4%
199831
 
2.1%
199930
 
2.1%
197629
 
2.0%
200829
 
2.0%
Other values (87)949
65.0%
(Missing)81
 
5.5%
ValueCountFrequency (%)
19001
 
0.1%
19061
 
0.1%
19081
 
0.1%
19103
 
0.2%
19142
 
0.1%
19152
 
0.1%
19165
 
0.3%
19182
 
0.1%
192014
1.0%
19213
 
0.2%
ValueCountFrequency (%)
20103
 
0.2%
200921
 
1.4%
200829
2.0%
200749
3.4%
200659
4.0%
200565
4.5%
200453
3.6%
200350
3.4%
200226
 
1.8%
200120
 
1.4%

GarageFinish
Categorical

Missing 

Distinct3
Distinct (%)0.2%
Missing81
Missing (%)5.5%
Memory size86.0 KiB
Unf
605 
RFn
422 
Fin
352 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4137
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf605
41.4%
RFn422
28.9%
Fin352
24.1%
(Missing)81
 
5.5%

Length

2025-11-07T15:52:30.970033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:31.093671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
unf605
43.9%
rfn422
30.6%
fin352
25.5%

Most occurring characters

ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Length

2025-11-07T15:52:31.231240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:31.367255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

GarageArea
Real number (ℝ)

Zeros 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:31.519873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2025-11-07T15:52:31.693760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.5%
44049
 
3.4%
57647
 
3.2%
24038
 
2.6%
48434
 
2.3%
52833
 
2.3%
28827
 
1.8%
40025
 
1.7%
26424
 
1.6%
48024
 
1.6%
Other values (431)1078
73.8%
ValueCountFrequency (%)
081
5.5%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
 
0.3%
2053
 
0.2%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%
11661
0.1%
11341
0.1%
10691
0.1%
10531
0.1%
10522
0.1%

GarageQual
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size84.6 KiB
TA
1311 
Fa
 
48
Gd
 
14
Ex
 
3
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1311
89.8%
Fa48
 
3.3%
Gd14
 
1.0%
Ex3
 
0.2%
Po3
 
0.2%
(Missing)81
 
5.5%

Length

2025-11-07T15:52:31.859825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:31.989997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta1311
95.1%
fa48
 
3.5%
gd14
 
1.0%
ex3
 
0.2%
po3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

GarageCond
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size84.6 KiB
TA
1326 
Fa
 
35
Gd
 
9
Po
 
7
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1326
90.8%
Fa35
 
2.4%
Gd9
 
0.6%
Po7
 
0.5%
Ex2
 
0.1%
(Missing)81
 
5.5%

Length

2025-11-07T15:52:32.138067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:32.270642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ta1326
96.2%
fa35
 
2.5%
gd9
 
0.7%
po7
 
0.5%
ex2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

PavedDrive
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
Y
1340 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Length

2025-11-07T15:52:32.420968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:32.551060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
y1340
91.8%
n90
 
6.2%
p30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

WoodDeckSF
Real number (ℝ)

Zeros 

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.244521
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:32.703916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.33879
Coefficient of variation (CV)1.3299319
Kurtosis2.9929509
Mean94.244521
Median Absolute Deviation (MAD)0
Skewness1.5413758
Sum137597
Variance15709.813
MonotonicityNot monotonic
2025-11-07T15:52:32.877413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
52.1%
19238
 
2.6%
10036
 
2.5%
14433
 
2.3%
12031
 
2.1%
16828
 
1.9%
14015
 
1.0%
22414
 
1.0%
20810
 
0.7%
24010
 
0.7%
Other values (264)484
33.2%
ValueCountFrequency (%)
0761
52.1%
122
 
0.1%
242
 
0.1%
262
 
0.1%
282
 
0.1%
301
 
0.1%
321
 
0.1%
331
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
8571
0.1%
7361
0.1%
7281
0.1%
6701
0.1%
6681
0.1%
6351
0.1%
5861
0.1%
5761
0.1%
5741
0.1%
5501
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:33.041755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2025-11-07T15:52:33.211119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0656
44.9%
3629
 
2.0%
4822
 
1.5%
2021
 
1.4%
4019
 
1.3%
4519
 
1.3%
2416
 
1.1%
3016
 
1.1%
6015
 
1.0%
3914
 
1.0%
Other values (192)633
43.4%
ValueCountFrequency (%)
0656
44.9%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
123
 
0.2%
151
 
0.1%
168
 
0.5%
172
 
0.1%
185
 
0.3%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%
3641
0.1%
3411
0.1%
3191
0.1%
3122
0.1%
3041
0.1%

EnclosedPorch
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95411
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:33.388515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.119149
Coefficient of variation (CV)2.7839502
Kurtosis10.430766
Mean21.95411
Median Absolute Deviation (MAD)0
Skewness3.0898719
Sum32053
Variance3735.5503
MonotonicityNot monotonic
2025-11-07T15:52:33.557806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01252
85.8%
11215
 
1.0%
966
 
0.4%
1925
 
0.3%
1445
 
0.3%
1205
 
0.3%
2165
 
0.3%
1564
 
0.3%
1164
 
0.3%
2524
 
0.3%
Other values (110)155
 
10.6%
ValueCountFrequency (%)
01252
85.8%
191
 
0.1%
201
 
0.1%
241
 
0.1%
301
 
0.1%
322
 
0.1%
342
 
0.1%
362
 
0.1%
371
 
0.1%
392
 
0.1%
ValueCountFrequency (%)
5521
0.1%
3861
0.1%
3301
0.1%
3181
0.1%
3011
0.1%
2941
0.1%
2931
0.1%
2911
0.1%
2861
0.1%
2801
0.1%

3SsnPorch
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:33.710163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.317331
Coefficient of variation (CV)8.5984939
Kurtosis123.66238
Mean3.409589
Median Absolute Deviation (MAD)0
Skewness10.304342
Sum4978
Variance859.50587
MonotonicityNot monotonic
2025-11-07T15:52:33.859194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01436
98.4%
1683
 
0.2%
1442
 
0.1%
1802
 
0.1%
2162
 
0.1%
2901
 
0.1%
1531
 
0.1%
961
 
0.1%
231
 
0.1%
1621
 
0.1%
Other values (10)10
 
0.7%
ValueCountFrequency (%)
01436
98.4%
231
 
0.1%
961
 
0.1%
1301
 
0.1%
1401
 
0.1%
1442
 
0.1%
1531
 
0.1%
1621
 
0.1%
1683
 
0.2%
1802
 
0.1%
ValueCountFrequency (%)
5081
0.1%
4071
0.1%
3201
0.1%
3041
0.1%
2901
0.1%
2451
0.1%
2381
0.1%
2162
0.1%
1961
0.1%
1821
0.1%

ScreenPorch
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060959
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:34.043255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.757415
Coefficient of variation (CV)3.7021159
Kurtosis18.439068
Mean15.060959
Median Absolute Deviation (MAD)0
Skewness4.1222137
Sum21989
Variance3108.8894
MonotonicityNot monotonic
2025-11-07T15:52:34.229418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01344
92.1%
1926
 
0.4%
1205
 
0.3%
2245
 
0.3%
1894
 
0.3%
1804
 
0.3%
1473
 
0.2%
903
 
0.2%
1603
 
0.2%
1443
 
0.2%
Other values (66)80
 
5.5%
ValueCountFrequency (%)
01344
92.1%
401
 
0.1%
531
 
0.1%
601
 
0.1%
631
 
0.1%
801
 
0.1%
903
 
0.2%
951
 
0.1%
991
 
0.1%
1002
 
0.1%
ValueCountFrequency (%)
4801
0.1%
4401
0.1%
4101
0.1%
3961
0.1%
3851
0.1%
3741
0.1%
3221
0.1%
3121
0.1%
2911
0.1%
2882
0.1%

PoolArea
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7589041
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:34.360436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.177307
Coefficient of variation (CV)14.562778
Kurtosis223.2685
Mean2.7589041
Median Absolute Deviation (MAD)0
Skewness14.828374
Sum4028
Variance1614.216
MonotonicityNot monotonic
2025-11-07T15:52:34.528809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01453
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01453
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
5761
 
0.1%
6481
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
7381
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
5191
 
0.1%
5121
 
0.1%
4801
 
0.1%
01453
99.5%

PoolQC
Categorical

Missing 

Distinct3
Distinct (%)42.9%
Missing1453
Missing (%)99.5%
Memory size91.3 KiB
Gd
Ex
Fa

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd

Common Values

ValueCountFrequency (%)
Gd3
 
0.2%
Ex2
 
0.1%
Fa2
 
0.1%
(Missing)1453
99.5%

Length

2025-11-07T15:52:34.679759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:34.807771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
gd3
42.9%
ex2
28.6%
fa2
28.6%

Most occurring characters

ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Fence
Categorical

Missing 

Distinct4
Distinct (%)1.4%
Missing1179
Missing (%)80.8%
Memory size90.8 KiB
MnPrv
157 
GdPrv
59 
GdWo
54 
MnWw
 
11

Length

Max length5
Median length5
Mean length4.7686833
Min length4

Characters and Unicode

Total characters1340
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv

Common Values

ValueCountFrequency (%)
MnPrv157
 
10.8%
GdPrv59
 
4.0%
GdWo54
 
3.7%
MnWw11
 
0.8%
(Missing)1179
80.8%

Length

2025-11-07T15:52:34.956601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:35.075452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
mnprv157
55.9%
gdprv59
 
21.0%
gdwo54
 
19.2%
mnww11
 
3.9%

Most occurring characters

ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

MiscFeature
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)7.4%
Missing1406
Missing (%)96.3%
Memory size91.2 KiB
Shed
49 
Gar2
 
2
Othr
 
2
TenC
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters216
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed

Common Values

ValueCountFrequency (%)
Shed49
 
3.4%
Gar22
 
0.1%
Othr2
 
0.1%
TenC1
 
0.1%
(Missing)1406
96.3%

Length

2025-11-07T15:52:35.213306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:35.335482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
shed49
90.7%
gar22
 
3.7%
othr2
 
3.7%
tenc1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

MiscVal
Real number (ℝ)

Skewed  Zeros 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.489041
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:35.457323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.12302
Coefficient of variation (CV)11.408001
Kurtosis701.00334
Mean43.489041
Median Absolute Deviation (MAD)0
Skewness24.476794
Sum63494
Variance246138.06
MonotonicityNot monotonic
2025-11-07T15:52:35.609581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01408
96.4%
40011
 
0.8%
5008
 
0.5%
7005
 
0.3%
4504
 
0.3%
6004
 
0.3%
20004
 
0.3%
12002
 
0.1%
4802
 
0.1%
155001
 
0.1%
Other values (11)11
 
0.8%
ValueCountFrequency (%)
01408
96.4%
541
 
0.1%
3501
 
0.1%
40011
 
0.8%
4504
 
0.3%
4802
 
0.1%
5008
 
0.5%
5601
 
0.1%
6004
 
0.3%
6201
 
0.1%
ValueCountFrequency (%)
155001
 
0.1%
83001
 
0.1%
35001
 
0.1%
25001
 
0.1%
20004
0.3%
14001
 
0.1%
13001
 
0.1%
12002
0.1%
11501
 
0.1%
8001
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219178
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:35.758172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7036262
Coefficient of variation (CV)0.42765918
Kurtosis-0.40410934
Mean6.3219178
Median Absolute Deviation (MAD)2
Skewness0.21205299
Sum9230
Variance7.3095947
MonotonicityNot monotonic
2025-11-07T15:52:35.897581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.3%
7234
16.0%
5204
14.0%
4141
9.7%
8122
8.4%
3106
7.3%
1089
 
6.1%
1179
 
5.4%
963
 
4.3%
1259
 
4.0%
Other values (2)110
7.5%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3106
7.3%
4141
9.7%
5204
14.0%
6253
17.3%
7234
16.0%
8122
8.4%
963
 
4.3%
1089
 
6.1%
ValueCountFrequency (%)
1259
 
4.0%
1179
 
5.4%
1089
 
6.1%
963
 
4.3%
8122
8.4%
7234
16.0%
6253
17.3%
5204
14.0%
4141
9.7%
3106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Length

2025-11-07T15:52:36.044528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:36.177877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Most occurring characters

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

SaleType
Categorical

Imbalance 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size84.5 KiB
WD
1267 
New
 
122
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.1582192
Min length2

Characters and Unicode

Total characters3151
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD1267
86.8%
New122
 
8.4%
COD43
 
2.9%
ConLD9
 
0.6%
ConLI5
 
0.3%
ConLw5
 
0.3%
CWD4
 
0.3%
Oth3
 
0.2%
Con2
 
0.1%

Length

2025-11-07T15:52:36.332341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:36.477588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
wd1267
86.8%
new122
 
8.4%
cod43
 
2.9%
conld9
 
0.6%
conli5
 
0.3%
conlw5
 
0.3%
cwd4
 
0.3%
oth3
 
0.2%
con2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

SaleCondition
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size90.2 KiB
Normal
1198 
Partial
125 
Abnorml
 
101
Family
 
20
Alloca
 
12

Length

Max length7
Median length6
Mean length6.1575342
Min length6

Characters and Unicode

Total characters8990
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal1198
82.1%
Partial125
 
8.6%
Abnorml101
 
6.9%
Family20
 
1.4%
Alloca12
 
0.8%
AdjLand4
 
0.3%

Length

2025-11-07T15:52:36.644415image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-07T15:52:36.777997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
normal1198
82.1%
partial125
 
8.6%
abnorml101
 
6.9%
family20
 
1.4%
alloca12
 
0.8%
adjland4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-11-07T15:52:36.941386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2025-11-07T15:52:37.135381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

Interactions

2025-11-07T15:51:47.896551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:32.280324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:36.375480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:40.999703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:47.545889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:54.911990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:01.136580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:07.966777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:15.219902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:21.813530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:28.907528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:35.457708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:43.376014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:49.996166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:57.557455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:04.272963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:11.822804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:18.380046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:24.835601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-07T15:50:52.963197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-07T15:51:06.198478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:12.877180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-07T15:51:26.083366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-07T15:51:41.359280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:48.130572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:32.433099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:36.518466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:41.144597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:47.793026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:55.117856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:01.367062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:08.183055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:15.435075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:22.056886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:29.117692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:35.672413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:43.594391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:50.222302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:57.773514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:04.482097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:12.041993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:18.593714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:25.062026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-07T15:49:14.776892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:21.365952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:28.433674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:35.040335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:42.901310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:49.545437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:57.065849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:03.837106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:11.413602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:17.940203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:24.414915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:31.099865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:37.784916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:45.635720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:52.447047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:59.244007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:05.787805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:12.378862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:19.054540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:25.653038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:33.985173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:40.858193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:47.453859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:54.535104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:36.222638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:40.828420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:47.302375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:48:54.689448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:00.922294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:07.736223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:14.969319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:21.584576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:28.671520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:35.244836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:43.145071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:49.734879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:49:57.294253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:04.032773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:11.614558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:18.154338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:24.620619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:31.306290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:38.010244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:45.853027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:52.691316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:50:59.446783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:05.984036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:12.640864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:19.265454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:25.857922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:34.191549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:41.087944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-07T15:51:47.676471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2025-11-07T15:51:55.256408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-07T15:51:56.254281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-07T15:51:57.319202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNaN0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNaN0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
5650RL85.014115PaveNaNIR1LvlAllPubInsideGtlMitchelNormNorm1Fam1.5Fin5519931995GableCompShgVinylSdVinylSdNaN0.0TATAWoodGdTANoGLQ732Unf064796GasAExYSBrkr79656601362101111TA5Typ0NaNAttchd1993.0Unf2480TATAY4030032000NaNMnPrvShed700102009WDNormal143000
6720RL75.010084PaveNaNRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520042005GableCompShgVinylSdVinylSdStone186.0GdTAPConcExTAAvGLQ1369Unf03171686GasAExYSBrkr1694001694102031Gd7Typ1GdAttchd2004.0RFn2636TATAY255570000NaNNaNNaN082007WDNormal307000
7860RLNaN10382PaveNaNIR1LvlAllPubCornerGtlNWAmesPosNNorm1Fam2Story7619731973GableCompShgHdBoardHdBoardStone240.0TATACBlockGdTAMnALQ859BLQ322161107GasAExYSBrkr110798302090102131TA7Typ2TAAttchd1973.0RFn2484TATAY235204228000NaNNaNShed350112009WDNormal200000
8950RM51.06120PaveNaNRegLvlAllPubInsideGtlOldTownArteryNorm1Fam1.5Fin7519311950GableCompShgBrkFaceWd ShngNaN0.0TATABrkTilTATANoUnf0Unf0952952GasAGdYFuseF102275201774002022TA8Min12TADetchd1931.0Unf2468FaTAY900205000NaNNaNNaN042008WDAbnorml129900
910190RL50.07420PaveNaNRegLvlAllPubCornerGtlBrkSideArteryArtery2fmCon1.5Unf5619391950GableCompShgMetalSdMetalSdNaN0.0TATABrkTilTATANoGLQ851Unf0140991GasAExYSBrkr1077001077101022TA5Typ2TAAttchd1939.0RFn1205GdTAY040000NaNNaNNaN012008WDNormal118000
IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
1450145190RL60.09000PaveNaNRegLvlAllPubFR2GtlNAmesNormNormDuplex2Story5519741974GableCompShgVinylSdVinylSdNaN0.0TATACBlockGdTANoUnf0Unf0896896GasATAYSBrkr89689601792002242TA8Typ0NaNNaNNaNNaN00NaNNaNY32450000NaNNaNNaN092009WDNormal136000
1451145220RL78.09262PaveNaNRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520082009GableCompShgCemntBdCmentBdStone194.0GdTAPConcGdTANoUnf0Unf015731573GasAExYSBrkr1578001578002031Ex7Typ1GdAttchd2008.0Fin3840TATAY0360000NaNNaNNaN052009NewPartial287090
14521453180RM35.03675PaveNaNRegLvlAllPubInsideGtlEdwardsNormNormTwnhsESLvl5520052005GableCompShgVinylSdVinylSdBrkFace80.0TATAPConcGdTAGdGLQ547Unf00547GasAGdYSBrkr1072001072101021TA5Typ0NaNBasment2005.0Fin2525TATAY0280000NaNNaNNaN052006WDNormal145000
1453145420RL90.017217PaveNaNRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story5520062006GableCompShgVinylSdVinylSdNaN0.0TATAPConcGdTANoUnf0Unf011401140GasAExYSBrkr1140001140001031TA6Typ0NaNNaNNaNNaN00NaNNaNY36560000NaNNaNNaN072006WDAbnorml84500
1454145520FV62.07500PavePaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story7520042005GableCompShgVinylSdVinylSdNaN0.0GdTAPConcGdTANoGLQ410Unf08111221GasAExYSBrkr1221001221102021Gd6Typ0NaNAttchd2004.0RFn2400TATAY01130000NaNNaNNaN0102009WDNormal185000
1455145660RL62.07917PaveNaNRegLvlAllPubInsideGtlGilbertNormNorm1Fam2Story6519992000GableCompShgVinylSdVinylSdNaN0.0TATAPConcGdTANoUnf0Unf0953953GasAExYSBrkr95369401647002131TA7Typ1TAAttchd1999.0RFn2460TATAY0400000NaNNaNNaN082007WDNormal175000
1456145720RL85.013175PaveNaNRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story6619781988GableCompShgPlywoodPlywoodStone119.0TATACBlockGdTANoALQ790Rec1635891542GasATAYSBrkr2073002073102031TA7Min12TAAttchd1978.0Unf2500TATAY34900000NaNMnPrvNaN022010WDNormal210000
1457145870RL66.09042PaveNaNRegLvlAllPubInsideGtlCrawforNormNorm1Fam2Story7919412006GableCompShgCemntBdCmentBdNaN0.0ExGdStoneTAGdNoGLQ275Unf08771152GasAExYSBrkr1188115202340002041Gd9Typ2GdAttchd1941.0RFn1252TATAY0600000NaNGdPrvShed250052010WDNormal266500
1458145920RL68.09717PaveNaNRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story5619501996HipCompShgMetalSdMetalSdNaN0.0TATACBlockTATAMnGLQ49Rec102901078GasAGdYFuseA1078001078101021Gd5Typ0NaNAttchd1950.0Unf1240TATAY3660112000NaNNaNNaN042010WDNormal142125
1459146020RL75.09937PaveNaNRegLvlAllPubInsideGtlEdwardsNormNorm1Fam1Story5619651965GableCompShgHdBoardHdBoardNaN0.0GdTACBlockTATANoBLQ830LwQ2901361256GasAGdYSBrkr1256001256101131TA6Typ0NaNAttchd1965.0Fin1276TATAY736680000NaNNaNNaN062008WDNormal147500